Chlorophyll Estimation from Multivariate Regression Analysis and Deep Learning Using Remote Sensing Data

Abstract

The Orinoco river is in Venezuela and flows into the Caribbean sea. The chlorophyll concentration in the ocean delta changes due to the dust deposition from the Orinoco river which affects the primary productivity. The wet and dry deposition measurements were obtained from Modern-Era Retrospective analysis for Research and Applications (MERRA) a NASA climate reanalysis of meteorology, atmospheric chemistry, land, ocean, and aerosols data on a broad range of weather and climate timescales and places. Researchers were not sure how wet and dry deposition from the Orinoco river has affected the chlorophyll concentration in the ocean. Aerosol optical depth (AOD), dry and wet deposition data were obtained from MERRA. Altimetry data of the Orinoco river and chlorophyll concentration data were also obtained from the Giovanni database from 2016 to March 2022. Linear regression analysis of altimetry and chlorophyll concentration showed that the latter did not depend on the water levels. Univariate models for each of the parameters of AOD, wet, and dry deposition were done. Bivariate models were done, adding one additional variable at a time, and finally a multivariate model was built for the prediction of chlorophyll concentration. From the analysis, it was seen that the multivariate models have a higher correlation between chlorophyll and the independent variables. Of all the variables, wet deposition is a better predictor of chlorophyll concentration. A deep learning neural network architecture is developed for performing the forecasting of chlorophyll concentration from past values.

Keywords

• remote sensing
• chlorophyll estimation
• regression
• deep learning neural network